Overview

Dataset statistics

Number of variables21
Number of observations100
Missing cells40
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory169.3 B

Variable types

NUM20
DATE1

Warnings

EBIT2EV_avg is highly correlated with OCFP_min and 1 other fieldsHigh correlation
OCFP_min is highly correlated with EBIT2EV_avg and 1 other fieldsHigh correlation
NCFP_avg is highly correlated with OCFP_min and 1 other fieldsHigh correlation
TO_100d_avg is highly correlated with TO_100d_minHigh correlation
TO_100d_min is highly correlated with TO_100d_avgHigh correlation
PPReversal_1_avg is highly correlated with PPReversal_5_min and 1 other fieldsHigh correlation
PPReversal_5_min is highly correlated with PPReversal_1_avg and 1 other fieldsHigh correlation
PPReversal_5_avg is highly correlated with PPReversal_5_min and 1 other fieldsHigh correlation
OCFP_min has 2 (2.0%) missing values Missing
TO_100d_min has 2 (2.0%) missing values Missing
EBIT2EV_avg has 2 (2.0%) missing values Missing
PPReversal_5_min has 2 (2.0%) missing values Missing
DAVOL20_avg has 2 (2.0%) missing values Missing
wgt_return_1m has 2 (2.0%) missing values Missing
TO_20d_avg has 2 (2.0%) missing values Missing
roe_q has 2 (2.0%) missing values Missing
turnover_vol_20d_max has 2 (2.0%) missing values Missing
DAVOL5_min has 2 (2.0%) missing values Missing
turnover_vol_100d_min has 2 (2.0%) missing values Missing
NCFP_avg has 2 (2.0%) missing values Missing
VSTD_30d_min has 2 (2.0%) missing values Missing
TO_100d_avg has 2 (2.0%) missing values Missing
std_1m has 2 (2.0%) missing values Missing
wgt_turn_3m has 2 (2.0%) missing values Missing
TO_5d_max has 2 (2.0%) missing values Missing
PPReversal_1_avg has 2 (2.0%) missing values Missing
turnover_vol_5d_min has 2 (2.0%) missing values Missing
PPReversal_5_avg has 2 (2.0%) missing values Missing
date has unique values Unique

Reproduction

Analysis started2020-12-16 04:25:10.313657
Analysis finished2020-12-16 04:25:53.714132
Duration43.4 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

date
Date

UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size800.0 B
Minimum2008-02-03 00:00:00
Maximum2009-12-27 00:00:00
2020-12-16T12:25:53.792394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:53.941184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

OCFP_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct89
Distinct (%)90.8%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.9110734969
Minimum-0.5123617749
Maximum3.589383307
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:54.080821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.5123617749
5-th percentile-0.5093374506
Q1-0.1037386643
median0.6299669912
Q31.610950525
95-th percentile3.589383307
Maximum3.589383307
Range4.101745082
Interquartile range (IQR)1.714689189

Descriptive statistics

Standard deviation1.229241118
Coefficient of variation (CV)1.349222781
Kurtosis-0.01019051644
Mean0.9110734969
Median Absolute Deviation (MAD)0.7963233836
Skewness0.9588727554
Sum89.2852027
Variance1.511033725
MonotocityNot monotonic
2020-12-16T12:25:54.223393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.58938330777.0%
 
1.11492254122.0%
 
0.633051580622.0%
 
0.508849563822.0%
 
3.08089994411.0%
 
0.456638678211.0%
 
0.810775388511.0%
 
-0.261088473411.0%
 
1.14001696511.0%
 
0.790918020411.0%
 
Other values (79)7979.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.512361774911.0%
 
-0.512295778611.0%
 
-0.510350327811.0%
 
-0.510031295511.0%
 
-0.509492046111.0%
 
ValueCountFrequency (%) 
3.58938330777.0%
 
3.47135107111.0%
 
3.43773512411.0%
 
3.31650422911.0%
 
3.30868235711.0%
 

TO_100d_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct96
Distinct (%)98.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.2770161579
Minimum-1.199134653
Maximum1.120401606
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:54.355526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.199134653
5-th percentile-1.128853755
Q1-0.8680485542
median-0.6123006895
Q30.3860689559
95-th percentile1.052794862
Maximum1.120401606
Range2.319536259
Interquartile range (IQR)1.25411751

Descriptive statistics

Standard deviation0.7180492831
Coefficient of variation (CV)-2.592084478
Kurtosis-1.145341054
Mean-0.2770161579
Median Absolute Deviation (MAD)0.4745371132
Skewness0.4898535929
Sum-27.14758348
Variance0.5155947729
MonotocityNot monotonic
2020-12-16T12:25:54.497140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.519848247922.0%
 
0.407726446522.0%
 
-0.769726713411.0%
 
-0.456447429611.0%
 
-0.742242956411.0%
 
0.355706928411.0%
 
-0.751186512811.0%
 
-0.854592803211.0%
 
0.49997564111.0%
 
0.0326784349311.0%
 
Other values (86)8686.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.19913465311.0%
 
-1.19512030411.0%
 
-1.17968815911.0%
 
-1.17933645511.0%
 
-1.15478945211.0%
 
ValueCountFrequency (%) 
1.12040160611.0%
 
1.09826781711.0%
 
1.09503074911.0%
 
1.07247298811.0%
 
1.06731091711.0%
 

EBIT2EV_avg
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct88
Distinct (%)89.8%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.8829398686
Minimum-0.4544254691
Maximum3.741685589
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:54.636256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.4544254691
5-th percentile-0.4298430176
Q1-0.04434191339
median0.3005635554
Q31.443308813
95-th percentile3.741685589
Maximum3.741685589
Range4.196111058
Interquartile range (IQR)1.487650727

Descriptive statistics

Standard deviation1.333694921
Coefficient of variation (CV)1.510516139
Kurtosis0.1539089971
Mean0.8829398686
Median Absolute Deviation (MAD)0.5490553434
Skewness1.199924719
Sum86.52810712
Variance1.778742142
MonotocityNot monotonic
2020-12-16T12:25:54.766691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.7416855891111.0%
 
-0.180998037211.0%
 
0.288067740511.0%
 
0.240907958611.0%
 
0.467228830511.0%
 
-0.302682601711.0%
 
-0.065458943811.0%
 
0.405914320411.0%
 
1.44543831811.0%
 
0.159605501911.0%
 
Other values (78)7878.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.454425469111.0%
 
-0.449917713411.0%
 
-0.441280545411.0%
 
-0.439647932811.0%
 
-0.439452115211.0%
 
ValueCountFrequency (%) 
3.7416855891111.0%
 
3.6275912611.0%
 
3.54775267111.0%
 
2.64374742211.0%
 
2.5074729411.0%
 

PPReversal_5_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct96
Distinct (%)98.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.08791186243
Minimum-2.637936887
Maximum2.613636047
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:54.900759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.637936887
5-th percentile-1.900421663
Q1-1.520273993
median0.01091578337
Q31.022506773
95-th percentile2.005676002
Maximum2.613636047
Range5.251572934
Interquartile range (IQR)2.542780767

Descriptive statistics

Standard deviation1.371180035
Coefficient of variation (CV)-15.5972129
Kurtosis-1.150658533
Mean-0.08791186243
Median Absolute Deviation (MAD)1.276105445
Skewness0.01325304174
Sum-8.615362519
Variance1.880134688
MonotocityNot monotonic
2020-12-16T12:25:55.042659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.760250562722.0%
 
-0.341314697922.0%
 
-1.78290431511.0%
 
1.44087462411.0%
 
0.894250056411.0%
 
-1.80866219411.0%
 
-1.69361210511.0%
 
-2.63793688711.0%
 
-0.087871836211.0%
 
-1.26150744811.0%
 
Other values (86)8686.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-2.63793688711.0%
 
-2.56322344911.0%
 
-2.41667836111.0%
 
-2.21385101811.0%
 
-1.91554854711.0%
 
ValueCountFrequency (%) 
2.61363604711.0%
 
2.47229977411.0%
 
2.18904147711.0%
 
2.07175343511.0%
 
2.0306007911.0%
 

DAVOL20_avg
Real number (ℝ)

MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.0897178297
Minimum-1.440670476
Maximum3.670662309
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:55.184621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.440670476
5-th percentile-1.20475532
Q1-0.9266183404
median-0.5197174067
Q30.4003541487
95-th percentile2.409691088
Maximum3.670662309
Range5.111332785
Interquartile range (IQR)1.326972489

Descriptive statistics

Standard deviation1.161843565
Coefficient of variation (CV)-12.94997404
Kurtosis1.649496216
Mean-0.0897178297
Median Absolute Deviation (MAD)0.5074765396
Skewness1.448158856
Sum-8.792347311
Variance1.34988047
MonotocityNot monotonic
2020-12-16T12:25:55.324459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.19414878811.0%
 
-0.829859163711.0%
 
0.332954711.0%
 
-0.262615388111.0%
 
2.39523499911.0%
 
0.51667914111.0%
 
-0.351349276211.0%
 
1.64105740311.0%
 
-0.96220990311.0%
 
0.00309710624311.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.44067047611.0%
 
-1.38973484211.0%
 
-1.34099940311.0%
 
-1.29977197611.0%
 
-1.26485899911.0%
 
ValueCountFrequency (%) 
3.67066230911.0%
 
3.55846347811.0%
 
3.22609773811.0%
 
2.89988122511.0%
 
2.49160892411.0%
 

wgt_return_1m
Real number (ℝ)

MISSING

Distinct33
Distinct (%)33.7%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.07219838539
Minimum-3.571536921
Maximum1.000728585
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:55.458124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-3.571536921
5-th percentile-2.423760007
Q1-0.3350588962
median0.3196757946
Q30.6745749908
95-th percentile0.9445587867
Maximum1.000728585
Range4.572265506
Interquartile range (IQR)1.009633887

Descriptive statistics

Standard deviation1.119552062
Coefficient of variation (CV)-15.50660802
Kurtosis2.461465147
Mean-0.07219838539
Median Absolute Deviation (MAD)0.3993402363
Skewness-1.731683786
Sum-7.075441768
Variance1.25339682
MonotocityNot monotonic
2020-12-16T12:25:55.589756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
0.199107911355.0%
 
-0.872256433544.0%
 
0.791311396444.0%
 
0.472900201944.0%
 
-1.93050524744.0%
 
-0.325164343744.0%
 
-1.04121542844.0%
 
0.674574990844.0%
 
-0.338357080444.0%
 
0.379288212744.0%
 
Other values (23)5757.0%
 
ValueCountFrequency (%) 
-3.57153692144.0%
 
-2.42376000744.0%
 
-1.93050524744.0%
 
-1.04121542844.0%
 
-0.872256433544.0%
 
ValueCountFrequency (%) 
1.00072858533.0%
 
1.00072858511.0%
 
0.944558786733.0%
 
0.874265973133.0%
 
0.799224721444.0%
 

TO_20d_avg
Real number (ℝ)

MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.2063481297
Minimum-1.093566067
Maximum1.718114784
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:55.719562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.093566067
5-th percentile-1.037211835
Q1-0.7718225987
median-0.3692245908
Q30.1891668571
95-th percentile1.207465946
Maximum1.718114784
Range2.811680852
Interquartile range (IQR)0.9609894557

Descriptive statistics

Standard deviation0.7151754998
Coefficient of variation (CV)-3.465868582
Kurtosis0.0404970915
Mean-0.2063481297
Median Absolute Deviation (MAD)0.4315390671
Skewness0.9519886342
Sum-20.22211671
Variance0.5114759955
MonotocityNot monotonic
2020-12-16T12:25:55.856038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0702850311211.0%
 
-0.555625474311.0%
 
-0.400146721711.0%
 
-1.0265736311.0%
 
-0.385886587411.0%
 
0.293421202711.0%
 
-0.88140845111.0%
 
0.171433474411.0%
 
-0.325721250911.0%
 
-0.500186087511.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.09356606711.0%
 
-1.0828401111.0%
 
-1.0630301811.0%
 
-1.0542035211.0%
 
-1.04761261511.0%
 
ValueCountFrequency (%) 
1.71811478411.0%
 
1.70029190611.0%
 
1.43934198711.0%
 
1.40060888311.0%
 
1.28508699211.0%
 

roe_q
Real number (ℝ)

MISSING

Distinct15
Distinct (%)15.3%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.2534706808
Minimum-1.103691441
Maximum1.385147577
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:55.978956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.103691441
5-th percentile-1.103691441
Q10.3046169506
median0.3805843601
Q30.7116745073
95-th percentile1.385147577
Maximum1.385147577
Range2.488839018
Interquartile range (IQR)0.4070575567

Descriptive statistics

Standard deviation0.759719366
Coefficient of variation (CV)2.997267232
Kurtosis-0.2800271004
Mean0.2534706808
Median Absolute Deviation (MAD)0.07596740949
Skewness-0.6872473567
Sum24.84012672
Variance0.5771735151
MonotocityNot monotonic
2020-12-16T12:25:56.106028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0.42848524741313.0%
 
0.38058436011212.0%
 
0.3614889831212.0%
 
0.30461695061212.0%
 
1.3851475771212.0%
 
-1.0739037271111.0%
 
0.79444704411010.0%
 
-1.10369144177.0%
 
-1.10369144122.0%
 
0.794447044122.0%
 
Other values (5)55.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.10369144177.0%
 
-1.10369144122.0%
 
-1.0739037271111.0%
 
0.30461695061212.0%
 
0.304616950611.0%
 
ValueCountFrequency (%) 
1.3851475771212.0%
 
0.811150179111.0%
 
0.794447044122.0%
 
0.79444704411010.0%
 
0.463356896911.0%
 

turnover_vol_20d_max
Real number (ℝ)

MISSING

Distinct95
Distinct (%)96.9%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.4701865406
Minimum-0.8916089389
Maximum3.997560143
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:56.233407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8916089389
5-th percentile-0.734535571
Q1-0.4133886017
median-0.08394323962
Q31.077400992
95-th percentile3.101571493
Maximum3.997560143
Range4.889169082
Interquartile range (IQR)1.490789594

Descriptive statistics

Standard deviation1.219721891
Coefficient of variation (CV)2.594123366
Kurtosis1.514867983
Mean0.4701865406
Median Absolute Deviation (MAD)0.5196314341
Skewness1.416418797
Sum46.07828098
Variance1.487721492
MonotocityNot monotonic
2020-12-16T12:25:56.375999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.99756014344.0%
 
-0.448644805711.0%
 
0.16273464411.0%
 
-0.667645931611.0%
 
-0.246359494611.0%
 
2.6012782511.0%
 
-0.202024030311.0%
 
-0.158990215311.0%
 
0.641509505211.0%
 
-0.50261283211.0%
 
Other values (85)8585.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.891608938911.0%
 
-0.826527408811.0%
 
-0.808074551511.0%
 
-0.806243146911.0%
 
-0.793836064811.0%
 
ValueCountFrequency (%) 
3.99756014344.0%
 
3.867009811.0%
 
2.96649414511.0%
 
2.85633711111.0%
 
2.6012782511.0%
 

DAVOL5_min
Real number (ℝ)

MISSING

Distinct97
Distinct (%)99.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.1355494176
Minimum-1.185903585
Maximum4.677212578
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:56.511186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.185903585
5-th percentile-1.071353839
Q1-0.8453494188
median-0.5356674032
Q30.3220200744
95-th percentile2.012710818
Maximum4.677212578
Range5.863116163
Interquartile range (IQR)1.167369493

Descriptive statistics

Standard deviation1.119368792
Coefficient of variation (CV)-8.258012554
Kurtosis5.546696319
Mean-0.1355494176
Median Absolute Deviation (MAD)0.3265144685
Skewness2.18363867
Sum-13.28384292
Variance1.252986493
MonotocityNot monotonic
2020-12-16T12:25:56.655017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.684085201222.0%
 
3.7394214611.0%
 
-1.16362830611.0%
 
-0.973012995511.0%
 
1.41527917811.0%
 
-0.392400184611.0%
 
0.484337615811.0%
 
-0.934139619711.0%
 
-0.703725256211.0%
 
2.02266262911.0%
 
Other values (87)8787.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.18590358511.0%
 
-1.16589318911.0%
 
-1.16362830611.0%
 
-1.14967284411.0%
 
-1.1489941311.0%
 
ValueCountFrequency (%) 
4.67721257811.0%
 
4.18499525611.0%
 
3.7394214611.0%
 
2.02266262911.0%
 
2.01593707511.0%
 

turnover_vol_100d_min
Real number (ℝ)

MISSING

Distinct97
Distinct (%)99.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.5905633083
Minimum-0.7261001796
Maximum2.505899331
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:56.792800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7261001796
5-th percentile-0.6387616794
Q1-0.2500021932
median0.2419249729
Q31.218094429
95-th percentile2.453673866
Maximum2.505899331
Range3.23199951
Interquartile range (IQR)1.468096622

Descriptive statistics

Standard deviation1.046067733
Coefficient of variation (CV)1.771304987
Kurtosis-0.7801689561
Mean0.5905633083
Median Absolute Deviation (MAD)0.5030362994
Skewness0.8050721513
Sum57.87520421
Variance1.094257702
MonotocityNot monotonic
2020-12-16T12:25:56.936499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.429004999222.0%
 
1.25445166211.0%
 
0.157906749611.0%
 
0.0399894141911.0%
 
-0.719916899711.0%
 
-0.258427052211.0%
 
2.3591127511.0%
 
0.0815119597211.0%
 
-0.245527563711.0%
 
2.37406138511.0%
 
Other values (87)8787.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.726100179611.0%
 
-0.72133145911.0%
 
-0.719916899711.0%
 
-0.696661022611.0%
 
-0.642802815411.0%
 
ValueCountFrequency (%) 
2.50589933111.0%
 
2.5042478311.0%
 
2.4794739211.0%
 
2.46360841411.0%
 
2.45528777111.0%
 

NCFP_avg
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.5398677824
Minimum-0.6399235897
Maximum3.325144336
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:57.079534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.6399235897
5-th percentile-0.6364907236
Q1-0.3148790713
median0.2869481457
Q31.04497776
95-th percentile2.834697799
Maximum3.325144336
Range3.965067925
Interquartile range (IQR)1.359856831

Descriptive statistics

Standard deviation1.033608848
Coefficient of variation (CV)1.914559234
Kurtosis0.506107445
Mean0.5398677824
Median Absolute Deviation (MAD)0.6278948676
Skewness1.125168696
Sum52.90704268
Variance1.068347251
MonotocityNot monotonic
2020-12-16T12:25:57.227042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.627591802411.0%
 
0.655263159711.0%
 
2.55860754511.0%
 
-0.317316023411.0%
 
0.238030434311.0%
 
0.339475597411.0%
 
-0.395178754511.0%
 
-0.266992660611.0%
 
2.54826487611.0%
 
-0.439424957411.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.639923589711.0%
 
-0.63929409511.0%
 
-0.638087940311.0%
 
-0.637859950811.0%
 
-0.637832605511.0%
 
ValueCountFrequency (%) 
3.32514433611.0%
 
3.23298857911.0%
 
2.91923153911.0%
 
2.85826177611.0%
 
2.85736785111.0%
 

VSTD_30d_min
Real number (ℝ)

MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.366382226
Minimum-0.843074586
Maximum0.3585725932
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:57.361045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.843074586
5-th percentile-0.8207432113
Q1-0.6763134896
median-0.427858952
Q3-0.1315527787
95-th percentile0.2366780795
Maximum0.3585725932
Range1.201647179
Interquartile range (IQR)0.5447607108

Descriptive statistics

Standard deviation0.356233354
Coefficient of variation (CV)-0.9722997697
Kurtosis-1.129472775
Mean-0.366382226
Median Absolute Deviation (MAD)0.2797231662
Skewness0.3855796221
Sum-35.90545815
Variance0.1269022025
MonotocityNot monotonic
2020-12-16T12:25:57.513282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.820346047511.0%
 
-0.473570017811.0%
 
-0.184899670411.0%
 
0.19915290711.0%
 
-0.0284246077811.0%
 
-0.621392397511.0%
 
-0.821702225611.0%
 
0.197175607711.0%
 
-0.634868347111.0%
 
0.0123912479211.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.84307458611.0%
 
-0.822182105811.0%
 
-0.822156138411.0%
 
-0.821702225611.0%
 
-0.820744221911.0%
 
ValueCountFrequency (%) 
0.358572593211.0%
 
0.290867920411.0%
 
0.269371786511.0%
 
0.257192101111.0%
 
0.243369360811.0%
 

TO_100d_avg
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.2746570259
Minimum-1.207786699
Maximum1.129936139
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:57.650466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.207786699
5-th percentile-1.138411636
Q1-0.8721758936
median-0.5646562188
Q30.3733618779
95-th percentile1.03392875
Maximum1.129936139
Range2.337722838
Interquartile range (IQR)1.245537771

Descriptive statistics

Standard deviation0.7194790241
Coefficient of variation (CV)-2.619554412
Kurtosis-1.159262924
Mean-0.2746570259
Median Absolute Deviation (MAD)0.5034003576
Skewness0.4774835098
Sum-26.91638854
Variance0.5176500661
MonotocityNot monotonic
2020-12-16T12:25:57.792384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.20778669911.0%
 
1.03064265411.0%
 
-0.892627303611.0%
 
-0.879396427911.0%
 
-0.866266445811.0%
 
-0.933121982211.0%
 
-0.15301487311.0%
 
-0.638400561711.0%
 
0.363069091911.0%
 
0.532287351111.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.20778669911.0%
 
-1.20095874711.0%
 
-1.18133705511.0%
 
-1.1637124211.0%
 
-1.15846403811.0%
 
ValueCountFrequency (%) 
1.12993613911.0%
 
1.10494403611.0%
 
1.07002898911.0%
 
1.0533995911.0%
 
1.0525499611.0%
 

std_1m
Real number (ℝ)

MISSING

Distinct31
Distinct (%)31.6%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.4665576394
Minimum-0.8272214051
Maximum2.579709752
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:57.919696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8272214051
5-th percentile-0.6971314034
Q1-0.2644773678
median0.508065126
Q31.053466569
95-th percentile1.956373845
Maximum2.579709752
Range3.406931157
Interquartile range (IQR)1.317943937

Descriptive statistics

Standard deviation0.7976258749
Coefficient of variation (CV)1.709597716
Kurtosis-0.5171618152
Mean0.4665576394
Median Absolute Deviation (MAD)0.5698973915
Skewness0.4127998352
Sum45.72264866
Variance0.6362070363
MonotocityNot monotonic
2020-12-16T12:25:58.060422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
-0.40689111355.0%
 
1.07796251744.0%
 
1.99683243544.0%
 
0.224264502544.0%
 
0.218270063844.0%
 
0.530908415944.0%
 
-0.697131403444.0%
 
0.212345223844.0%
 
-0.420022120344.0%
 
0.673510519644.0%
 
Other values (21)5757.0%
 
ValueCountFrequency (%) 
-0.827221405133.0%
 
-0.697131403444.0%
 
-0.455009399544.0%
 
-0.420022120344.0%
 
-0.40689111355.0%
 
ValueCountFrequency (%) 
2.57970975211.0%
 
1.99683243544.0%
 
1.94923409311.0%
 
1.80643906944.0%
 
1.58098168344.0%
 

wgt_turn_3m
Real number (ℝ)

MISSING

Distinct12
Distinct (%)12.2%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.1288533887
Minimum-1.368178243
Maximum2.152292152
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:58.198855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.368178243
5-th percentile-1.368178243
Q1-1.124785663
median-0.1909404772
Q3-0.01572624256
95-th percentile2.152292152
Maximum2.152292152
Range3.520470395
Interquartile range (IQR)1.109059421

Descriptive statistics

Standard deviation1.068280632
Coefficient of variation (CV)-8.290667735
Kurtosis0.2982224523
Mean-0.1288533887
Median Absolute Deviation (MAD)0.6365523909
Skewness0.9701491949
Sum-12.62763209
Variance1.141223509
MonotocityNot monotonic
2020-12-16T12:25:58.317390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
-0.015726242561313.0%
 
-1.3681782431313.0%
 
2.1522921521313.0%
 
-1.2106456021212.0%
 
-0.051692737861212.0%
 
-0.19094047721212.0%
 
0.445611913799.0%
 
-0.867205846488.0%
 
-0.732739407233.0%
 
-0.283483310711.0%
 
Other values (2)22.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.3681782431313.0%
 
-1.2106456021212.0%
 
-0.867205846488.0%
 
-0.732739407233.0%
 
-0.283483310711.0%
 
ValueCountFrequency (%) 
2.1522921521313.0%
 
0.445611913799.0%
 
0.422455604611.0%
 
-0.015726242561313.0%
 
-0.051692737861212.0%
 

TO_5d_max
Real number (ℝ)

MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.1660720665
Minimum-1.037799518
Maximum2.696688707
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:58.453059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.037799518
5-th percentile-0.9778401239
Q1-0.703367297
median-0.4458459468
Q30.2059888151
95-th percentile1.468951694
Maximum2.696688707
Range3.734488225
Interquartile range (IQR)0.9093561121

Descriptive statistics

Standard deviation0.8032451142
Coefficient of variation (CV)-4.836726194
Kurtosis2.197162581
Mean-0.1660720665
Median Absolute Deviation (MAD)0.3876203171
Skewness1.525239262
Sum-16.27506252
Variance0.6452027135
MonotocityNot monotonic
2020-12-16T12:25:58.608899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.448722128711.0%
 
0.0915877806711.0%
 
-0.183445885311.0%
 
-0.442969764911.0%
 
-0.725385303811.0%
 
0.753286308511.0%
 
0.0419433761311.0%
 
-0.751843559511.0%
 
-0.678181890911.0%
 
-0.975518236911.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-1.03779951811.0%
 
-1.02894519711.0%
 
-1.01838283611.0%
 
-1.00507190611.0%
 
-0.990033302711.0%
 
ValueCountFrequency (%) 
2.69668870711.0%
 
2.24623739511.0%
 
2.101806611.0%
 
2.03809213611.0%
 
1.9340391411.0%
 

PPReversal_1_avg
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.04477586103
Minimum-2.566929553
Maximum2.833251014
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:58.756750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.566929553
5-th percentile-1.881075231
Q1-1.480362381
median0.04622679091
Q30.9538750666
95-th percentile2.174427223
Maximum2.833251014
Range5.400180567
Interquartile range (IQR)2.434237448

Descriptive statistics

Standard deviation1.381047911
Coefficient of variation (CV)-30.84358133
Kurtosis-1.068068013
Mean-0.04477586103
Median Absolute Deviation (MAD)1.282615121
Skewness0.09096469439
Sum-4.388034381
Variance1.907293333
MonotocityNot monotonic
2020-12-16T12:25:58.920665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.86067306911.0%
 
-1.71460642211.0%
 
-1.35612450411.0%
 
0.849255918411.0%
 
1.34336085811.0%
 
0.0817629476211.0%
 
-1.52542299311.0%
 
1.91424539711.0%
 
-1.59092741711.0%
 
0.567925061311.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-2.56692955311.0%
 
-2.36166434411.0%
 
-2.3485973311.0%
 
-1.98219124111.0%
 
-1.88847406411.0%
 
ValueCountFrequency (%) 
2.83325101411.0%
 
2.48579183811.0%
 
2.48051452911.0%
 
2.43398546111.0%
 
2.24439910611.0%
 

turnover_vol_5d_min
Real number (ℝ)

MISSING

Distinct92
Distinct (%)93.9%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean0.2879208934
Minimum-0.8631746376
Maximum3.614485608
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:59.079755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8631746376
5-th percentile-0.7073484146
Q1-0.4463132405
median-0.06724867411
Q30.5078349129
95-th percentile3.614485608
Maximum3.614485608
Range4.477660246
Interquartile range (IQR)0.9541481534

Descriptive statistics

Standard deviation1.088917806
Coefficient of variation (CV)3.78200343
Kurtosis3.063247486
Mean0.2879208934
Median Absolute Deviation (MAD)0.4032574044
Skewness1.817322077
Sum28.21624755
Variance1.185741989
MonotocityNot monotonic
2020-12-16T12:25:59.233475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.61448560866.0%
 
1.47802702622.0%
 
0.697155129111.0%
 
-0.047336527711.0%
 
1.46809793211.0%
 
-0.737036210411.0%
 
-0.295958433711.0%
 
-0.560312206111.0%
 
-0.532649731611.0%
 
-0.447417639611.0%
 
Other values (82)8282.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-0.863174637611.0%
 
-0.839792177611.0%
 
-0.782968804111.0%
 
-0.767983147211.0%
 
-0.737036210411.0%
 
ValueCountFrequency (%) 
3.61448560866.0%
 
1.90449890711.0%
 
1.87155125811.0%
 
1.80958011611.0%
 
1.63819613511.0%
 

PPReversal_5_avg
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean-0.0496011941
Minimum-2.588950875
Maximum2.748450478
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2020-12-16T12:25:59.378122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.588950875
5-th percentile-1.922964326
Q1-1.474787032
median0.07212175802
Q30.9497681293
95-th percentile2.060007607
Maximum2.748450478
Range5.337401353
Interquartile range (IQR)2.424555162

Descriptive statistics

Standard deviation1.379583228
Coefficient of variation (CV)-27.81350839
Kurtosis-1.125308175
Mean-0.0496011941
Median Absolute Deviation (MAD)1.271765074
Skewness0.04764261348
Sum-4.860917022
Variance1.903249884
MonotocityNot monotonic
2020-12-16T12:25:59.534812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.32835638811.0%
 
0.930920491511.0%
 
0.575118140911.0%
 
-0.178565527411.0%
 
-2.44226560311.0%
 
-0.429704151911.0%
 
-1.74191869611.0%
 
-1.45244867411.0%
 
0.813692205611.0%
 
-2.58895087511.0%
 
Other values (88)8888.0%
 
(Missing)22.0%
 
ValueCountFrequency (%) 
-2.58895087511.0%
 
-2.44226560311.0%
 
-2.33770168211.0%
 
-1.96491319111.0%
 
-1.92449150211.0%
 
ValueCountFrequency (%) 
2.74845047811.0%
 
2.63360809811.0%
 
2.33177434711.0%
 
2.1825308711.0%
 
2.06276495511.0%
 

Interactions

2020-12-16T12:25:14.283951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.362534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.441009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.521015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.598370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.677620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.753218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.834585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.912741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:14.996939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.072448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.163054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.247282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.326565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.401515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.482389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.563808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.645623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.723491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.799341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:15.878451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-16T12:25:35.194707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.296260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.394362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.490967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.598515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.699784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.798926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.895541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:35.994862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.094836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.200097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.305649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.399952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.498672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.586082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.677388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.766518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.864261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:36.959606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.059950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.153622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.262493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.371631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.471017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.579819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.681384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.801433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.899588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:37.996225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.090938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.196695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.291978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.383005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.476069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.561206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.647003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.730342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.822335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:38.912965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.001858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.100156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.187470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.274894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.364803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.456143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.550872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.643668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.729448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.823016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:39.915779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.008658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.098038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.190943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.279306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.371303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.462839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.553643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.645624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.736291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.831403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:40.927490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.025139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.122784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.215089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.313220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.412504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.507640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.598532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.694146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.792459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.901894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:41.998839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.092456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.198604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.290996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.387904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.479331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.573318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.669892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.763404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.868250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:42.965706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.077142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.170662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.270128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.373277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.482333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.575241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.675649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.773753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.879459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:43.974684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.069850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.179695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.271123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.369561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.462775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.555687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.654013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.750309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.849829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:44.946454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.051791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.153785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.251216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.357790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.457327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.551348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.651278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.750728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.852190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:45.949676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.045964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.151914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.239558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.333517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.423104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.512526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.601458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.691582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.781439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.881195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:46.974210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.071440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.182176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.286946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.388073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.482501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.579679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.683290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.781554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.879467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:47.971423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.068909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.162199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.252369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.338146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.428880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.519470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.607165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:48.697352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:49.476523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:49.580600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:49.671830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:49.764609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:49.863396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:49.959390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.050089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.152467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.249502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.347174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.440821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.528880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.620040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.710757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.804759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.898654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:50.989769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.084713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.186096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.278969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.377817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.471854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.564314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.662196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.760293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.857600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:51.952411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:52.050376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:52.162554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:52.261372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:52.357292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:52.449129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-16T12:25:59.699953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-16T12:25:59.919220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-16T12:26:00.152043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-16T12:26:00.363229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-16T12:25:52.668293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:52.932834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:53.255455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-16T12:25:53.586509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

dateOCFP_minTO_100d_minEBIT2EV_avgPPReversal_5_minDAVOL20_avgwgt_return_1mTO_20d_avgroe_qturnover_vol_20d_maxDAVOL5_minturnover_vol_100d_minNCFP_avgVSTD_30d_minTO_100d_avgstd_1mwgt_turn_3mTO_5d_maxPPReversal_1_avgturnover_vol_5d_minPPReversal_5_avg
02008-02-03-0.509492-0.246935-0.4281470.3432380.0140640.270159-0.068524-1.1036911.447075-0.4323701.109023-0.6362540.197343-0.2443152.5797100.422456-0.2680630.0053760.8708950.328541
12008-02-10-0.509201-0.303358-0.4394520.2599640.0173810.321136-0.097561-1.1036911.496444-0.6846761.019450-0.6378330.199153-0.3045771.580982-0.015726-0.6619130.2266180.3990440.140569
22008-02-17-0.510031-0.362257-0.4412810.265381-0.0168370.321136-0.143540-1.1036911.621900-0.8777900.998440-0.6380880.207008-0.3636211.580982-0.015726-0.7518440.237049-0.0907470.167181
32008-02-24-0.512296-0.411731-0.4499180.407365-0.3513490.321136-0.363939-1.1036911.628011-0.8660840.987627-0.6392940.235497-0.4117221.580982-0.015726-0.7004520.369984-0.1168460.341674
42008-03-02-0.510350-0.456447-0.4396480.190476-0.8322070.321136-0.666499-1.1036910.069013-0.9341400.993574-0.6378600.257192-0.4572491.580982-0.015726-0.7253850.081763-0.3790290.143811
52008-03-09-0.512362-0.583020-0.4544250.268717-0.964629-1.041215-0.771374-1.103691-0.451608-0.8386630.415465-0.639924-0.271453-0.5766391.806439-0.015726-0.6576130.2992830.3352800.236708
62008-03-16-0.509310-0.652183-0.426951-0.237959-0.919963-1.041215-0.787273-1.103691-0.479573-0.8001930.384985-0.636087-0.579243-0.6384011.806439-0.015726-0.695236-0.334743-0.453391-0.070069
72008-03-23-0.498706-0.713274-0.365395-1.268872-0.857041-1.041215-0.782128-1.103691-0.432792-0.6427600.244989-0.627491-0.629252-0.6932691.806439-0.015726-0.691113-1.3022960.212908-1.052657
82008-03-30-0.497379-0.774164-0.366119-1.281155-0.838391-1.041215-0.791446-1.103691-0.431675-0.8103200.148191-0.627592-0.627549-0.7770441.806439-0.015726-0.761994-1.243792-0.456464-1.287661
92008-04-06-0.492385-0.7865000.003254-1.711602-0.735404-0.504177-0.7920090.319912-0.202024-0.8430310.152018-0.405844-0.607374-0.7996921.949234-0.015726-0.678182-1.874435-0.447418-1.588799

Last rows

dateOCFP_minTO_100d_minEBIT2EV_avgPPReversal_5_minDAVOL20_avgwgt_return_1mTO_20d_avgroe_qturnover_vol_20d_maxDAVOL5_minturnover_vol_100d_minNCFP_avgVSTD_30d_minTO_100d_avgstd_1mwgt_turn_3mTO_5d_maxPPReversal_1_avgturnover_vol_5d_minPPReversal_5_avg
902009-10-251.6893830.3188340.9964700.189313-0.7885571.000729-0.2258540.361489-0.277991-0.8022240.0290851.066365-0.3314610.347233-0.264477-1.368178-0.5075810.211654-0.5326500.156557
912009-11-011.7300770.1077881.0322870.273896-1.0282921.000729-0.4792560.361489-0.563988-0.895706-0.1622181.101684-0.3362900.155173-0.264477-1.368178-0.6079300.112539-0.7370360.184393
922009-11-081.4651480.0152840.8577110.357584-0.9288370.674575-0.4053750.361489-0.333607-0.695572-0.2607890.929535-0.3160520.0262470.830339-0.8672060.1288700.8764230.4437830.565520
932009-11-151.496588-0.0109840.8574641.106651-0.7903640.674575-0.3119830.361489-0.324958-0.527117-0.2624230.929292-0.337963-0.0222860.830339-0.8672060.0915880.849256-0.1383311.003178
942009-11-221.381724-0.0042170.8283451.040318-0.6239830.674575-0.2014500.361489-0.160469-0.399521-0.2615280.900577-0.319414-0.0174160.830339-0.8672060.2034810.9350200.0399630.935075
952009-11-291.3898940.0365990.8211160.969074-0.1203360.6745750.0717980.361489-0.1589900.522106-0.2442170.893449-0.1733700.0257410.830339-0.8672060.3540040.8129130.2640880.954666
962009-12-061.4740330.0530860.8454170.6996030.1551840.1746010.1950780.361489-0.2501730.317206-0.2455280.917412-0.1579930.0368510.587083-0.8672060.2230350.5679250.2839370.638295
972009-12-131.4519650.0326780.8538210.5425630.4415920.0496080.3857260.361489-0.1223600.941960-0.3089060.925700-0.1269040.0518320.526269-0.8672060.6677210.4041390.7935710.473646
982009-12-201.617263-0.0580910.984839-0.0878720.4228210.0496080.3736590.361489-0.129859-0.228023-0.3714291.054896-0.150165-0.0557450.526269-0.8672060.220993-0.201380-0.0322230.062684
992009-12-271.592014-0.1415871.024252-0.3942720.1450450.0496080.1714330.361489-0.139235-0.392400-0.4902491.093761-0.145500-0.1530150.526269-0.867206-0.267441-0.333841-0.443000-0.429704